Mirjalili et al [1]} proposed a new evolutionary algorithm based on the concept of grey wolf packs named Grey Wolf Optimizer (GWO). GWO outperforms other evolutionary algorithms in searching for the solution of nonlinear functions in multidimensional space. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves. The leadership hierarchy is simulated based on the behavior of different types in pack of wolves, such as the alpha, beta, delta, and omega. Basically, the GWO, inspired by grey wolf hunting procedure, involves three main phases. First, seeking for prey, which is called exploration, encircling prey and lastly, attacking prey, which provides exploitation. This process ensures that scientists identify the global optimum to tackle optimization problems.
